{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd06dbac-fe7b-43f1-a9e7-6b6a5b39ad52",
   "metadata": {},
   "source": [
    "# Demonstration of using Chemprop featurizer with DGL and PyTorch Geometric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c55d990",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/chemprop/chemprop/blob/main/examples/use_featurizer_with_other_libraries.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb316f5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install chemprop from GitHub if running in Google Colab\n",
    "import os\n",
    "\n",
    "if os.getenv(\"COLAB_RELEASE_TAG\"):\n",
    "    try:\n",
    "        import chemprop\n",
    "    except ImportError:\n",
    "        !git clone https://github.com/chemprop/chemprop.git\n",
    "        %cd chemprop\n",
    "        !pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0\n",
    "        !pip install .\n",
    "        !pip install  dgl -f https://data.dgl.ai/wheels/torch-2.4/repo.html\n",
    "        !pip install torch_geometric\n",
    "        %cd examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40cfeccb-bfec-4aef-a09b-929903455cce",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77d50745-e204-4a53-9e32-5c585caa1b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from sklearn.decomposition import PCA\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "from chemprop import data, featurizers, models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6301b0e9-d2f4-41b5-9e05-726c09ae1565",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3139fce1-cd87-4c56-9b65-ad13b6698d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_path = Path(\"..\") / \"tests\" / \"data\" / \"smis.csv\"\n",
    "smiles_column = \"smiles\"\n",
    "df_test = pd.read_csv(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caddf77e-317c-4dc1-9ddc-77a972ca58dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MoleculeDatapoint(mol=<rdkit.Chem.rdchem.Mol object at 0x7ec2273a1fc0>, y=None, weight=1.0, gt_mask=None, lt_mask=None, x_d=None, x_phase=None, name='Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14', V_f=None, E_f=None, V_d=None),\n",
       " MoleculeDatapoint(mol=<rdkit.Chem.rdchem.Mol object at 0x7ec2273a1ee0>, y=None, weight=1.0, gt_mask=None, lt_mask=None, x_d=None, x_phase=None, name='COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)CCc3ccccc23', V_f=None, E_f=None, V_d=None),\n",
       " MoleculeDatapoint(mol=<rdkit.Chem.rdchem.Mol object at 0x7ec2273a1e00>, y=None, weight=1.0, gt_mask=None, lt_mask=None, x_d=None, x_phase=None, name='COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl', V_f=None, E_f=None, V_d=None),\n",
       " MoleculeDatapoint(mol=<rdkit.Chem.rdchem.Mol object at 0x7ec2273a1d20>, y=None, weight=1.0, gt_mask=None, lt_mask=None, x_d=None, x_phase=None, name='OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(Cl)sc4[nH]3', V_f=None, E_f=None, V_d=None),\n",
       " MoleculeDatapoint(mol=<rdkit.Chem.rdchem.Mol object at 0x7ec2273a1c40>, y=None, weight=1.0, gt_mask=None, lt_mask=None, x_d=None, x_phase=None, name='Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)NCC#N)c1', V_f=None, E_f=None, V_d=None)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(test_path)\n",
    "\n",
    "smis = df_test[smiles_column]\n",
    "\n",
    "test_data = [data.MoleculeDatapoint.from_smi(smi) for smi in smis]\n",
    "test_data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0b1062-674d-41ad-8e06-1c925ca158f6",
   "metadata": {},
   "source": [
    "## Featurize molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77b7159f-ea91-4b0b-8cd1-5c7c64d02fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "featurizer = featurizers.SimpleMoleculeMolGraphFeaturizer()\n",
    "molgraphs = [featurizer(data.mol) for data in test_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba9f69aa-e676-463f-bafd-84f4a75a357a",
   "metadata": {},
   "source": [
    "# Use Chemprop featurizer with DGL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b5b24e61-67b9-42b7-a7ee-3bad644f18ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install DGL separately if not already installed\n",
    "# see https://www.dgl.ai/pages/start.html\n",
    "import dgl\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ca20930-94ea-4399-b262-e6b0cc6bff2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_molgraph_to_dgl_graph(mg):\n",
    "    \"\"\"\n",
    "    Takes a Chemprop molgraph from featurizer and converts it to a DGL graph object.\n",
    "    Atom features are saved in 'n' and edge features in 'e'\n",
    "    \"\"\"\n",
    "    # Instantiate a graph from the edges\n",
    "    g = dgl.graph((mg.edge_index[0], mg.edge_index[1]), num_nodes=mg.V.shape[0])\n",
    "\n",
    "    # Assign features\n",
    "    g.ndata[\"n\"] = torch.tensor(mg.V)\n",
    "    g.edata[\"e\"] = torch.tensor(mg.E)\n",
    "    return g\n",
    "\n",
    "\n",
    "def visualize_dgl_graph(g):\n",
    "    \"\"\"\n",
    "    Visualize a DGL graph object.\n",
    "    Adapted from https://docs.dgl.ai/en/0.2.x/tutorials/basics/1_first.html\n",
    "    \"\"\"\n",
    "    nx_G = g.to_networkx()\n",
    "    pos = nx.kamada_kawai_layout(nx_G)\n",
    "    nx.draw(nx_G, pos, with_labels=True, node_color=[[0.5, 0.5, 0.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "954173cb-1913-4790-94ee-8705a1bce998",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the molgraphs to DGL graphs\n",
    "gs = [convert_molgraph_to_dgl_graph(x) for x in molgraphs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "183f5c98-7586-4c78-b5cb-0add1b82b220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ec2273a1fc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 0  # Feel free to change this to visualize different graphs.\n",
    "\n",
    "# Visualize the graphs\n",
    "display(test_data[idx].mol)\n",
    "visualize_dgl_graph(gs[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db4b56f6-2aa4-4dcf-bf14-c7a64d88592b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n': tensor([[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.1201],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 1.0000, 0.1401],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 1.0000, 0.1201],\n",
      "        ...,\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 1.0000, 0.1201],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 1.0000, 0.1201],\n",
      "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 1.0000, 0.1201]])}\n",
      "{'e': tensor([[0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.]],\n",
      "       dtype=torch.float64)}\n"
     ]
    }
   ],
   "source": [
    "# Examine the features\n",
    "print(gs[idx].ndata)\n",
    "print(gs[idx].edata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9958ec7f-fb10-4f42-a6f4-9b3806fcadcc",
   "metadata": {},
   "source": [
    "# Use Chemprop featurizer with PyTorch Geometric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "17f56f95-3df2-466e-b8c1-f9cea1eeb927",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install with https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html\n",
    "import torch_geometric\n",
    "from torch_geometric.data import Data\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cb5d0c8-88c2-43dd-8f49-84df9b80624d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_molgraph_to_pyg_graph(mg):\n",
    "    \"\"\"\n",
    "    Takes a Chemprop molgraph from featurizer and converts it to a PyTorch Geometric graph.\n",
    "    \"\"\"\n",
    "    # Instantiate a graph from the edges\n",
    "    data = Data(edge_index=torch.from_numpy(mg.edge_index), x=mg.V, edge_attr=mg.E)\n",
    "    return data\n",
    "\n",
    "\n",
    "def visualize_pyg_graph(g):\n",
    "    \"\"\"\n",
    "    Visualize a PyTorch Geometric graph object.\n",
    "    \"\"\"\n",
    "    nx_G = torch_geometric.utils.to_networkx(g, to_undirected=False)\n",
    "    pos = nx.kamada_kawai_layout(nx_G)\n",
    "    nx.draw(nx_G, pos, with_labels=True, node_color=[[0.5, 0.5, 0.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83c1c816-7a5a-4ca0-9d06-59b796d5cee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the molgraphs to PyG graphs\n",
    "pygs = [convert_molgraph_to_pyg_graph(x) for x in molgraphs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "419558f2-434b-43c1-ad12-0fcda9e02bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QsqenZ2xsrNCK9KWysvLhDktjxowB8M033wgoTH98fX0BREdH83I1MSwI+tFkZmZyI66uru7u7v379z99+jQtn2WaUOVZWVnciI+PT4MRholjZWWlUCjOnj07cODAq1evjhkzZu7cuWVlZULraju7d+8uKSkZPHgwvVsCcO3ataNHj9ra2tIQKDMlLi4uMzOzW7duNMdJfyzKRh+2HqlUGhUVlZCQQLOVTJaHlTMbNVP69ev3559/0mXphg0bfH19f/31V6FFtZFNmzYB4HqRAdi8eTOtk0RXqWYKfV+zZs2SSCT8XJGXNa2JQHdtpkyZIrSQVpOUlATgySef5Ebo39748eMFVMXQh7S0NC4NLyQkxOxaw+bk5IhEovbt23MnnxqNhoZjm/Ve07179+zs7EQiUXZ2Nl/XtKjV6MM39eaCj48PzavTaDTcCMzzvTAoffr0SUhIUKlUtMPCk08+uXv3bqFFtQJaqSc0NJSrKX7kyJHc3FxPT0+aH2im/Pzzz5WVlUFBQT169ODtonz5sSlQWVkpFoutrKwM1PnDoND2sNw3pFqttra2FovFFnPm+48lJyeHVhEDEBwcrNsjyGS5du1a586dAZw4cYIbzM/PX758+YYNGwQUpj8BAQHUTHm8pkXZKCGE7oFmZWUJLaTV0BRV3aNDurhm3e4sAK1Wq1KpOnToAKBjx466JdlNiurqai7r2tXV1d7e/tKlS0KL4pPz58/TfwJ+g8ct6qYe5nwy8/COhPnuUTAaIBKJ5HJ5ZmbmpEmT7t279913F4ODceOG0LJ0SE5Onj9/vpub26RJk6KioiQSiUQiqaioeOGFFyoqKoRWxxtcmjWXAMkLlmaj5ms9D38BmO97YTSKm5vbr7/+unnzZq12dXQ0+vbFxo0gREhJJSUlGzZsCAgICAgIoIUo/fz8lErljRs3MjIy+vTpk5aWRms7CKmSJ2pqanbs2AFg9uzZPF+ax5WtKfDdd98BeP3114UW0mqOHDkCYOTIkdzI5s2bAbzyyivCiWIYhMJCMnUqAQhA/vUvYvz7ZlqVIiQkhCYL4K+qFElJSbpPu3TpEu23YRnFxn7++WcYpsuZpdno77//DiAwMFBoIa0mNzcXgIuLCzeSkJBgoH91hikQGUk6dyYAsbMjSiX5qyCyYcnMzFy6dCmNW4JOjbSmqu4ePnyY9tsw2YZLLWf06NHgo7row1iajd68eROAk5OT0EJajVartbe3B8BV+C8pKQHQvn17HlsYMkyK4mIil9cvS4cNIxkZhpro3j2iUpExY0K529DevXuvWbOmJUUbVq5cCaBDhw4XL140lD7Dc+XKFbFY3K5du1Z10GghlmajhBAa5lZUVCS0kFbzcE0AWvZft68hw/KIjibu7gQgtrZk6VLCY7SeRkPi44lcTtq3JwAZMWKng4NDWFhYS/pHcGi1WlrzwcfH5969e7yJMy4ffvghDFbD1wJtlNbl1o13MxemTZsGYMuWLdzIiBEjADSoWcWwPO7dI3I5EYkIQPz9yd+3KNvC1atk6VLi4VG/1BWLyejRZNeu0rYF+pSXl/ft2xfApEmTNBqNvuKMDpd/ZaD6RJZ2Ug9zPuBmmfX/WBwdoVLh+HH06oXUVAwejEWLUFMDADk5SEqq/5kjIwNXrjRynepqREVh4kT07Illy3DtGrp3h0KBy5cRG4vQUIe2BfrY29vv37/f2dl5//79n376aRuuICyHDh3Kzc318vKi6xLesUAbNV/refgLwHzfC6MNjBiB5GS88w60WqxejaefRmoqPvgAAwdi5cq/PTMsDEuW/G0kKQnvvovu3REaiv/9D1ZWCAnB/v24dg1KJby89NXm4eGxY8cOqVT66aefmldWK/6qRTJnzhxau513mI2aEA/n0ZvvyvphwsPDHRwcrKysRo4ceaXRpRQDsLfH118jPh6+vsjIAC2xYGODNWvQ6G9BYSHWrUP//hg4EF99hbt3ERCA8HDcvInISEycCL5qGAGQyWSrVq0ihMyaNevixYu8XdfAFBUVRUdHS6XSGTNmGGoOQ+wUCAvtt+Xt7S20kFZDawJYW1ur1Wo6kp2dDcDd3V1YYXqi0Wg+/vhj3YWAWCweM2bMzp07udYUjAZUVpJDhwgh5IUXyIgRJCCAjBpFuGOhgADy0kskPp5IpfW7n66u5IMPDHjWz0F38Hv16tWgQ5zJ8vnnnwOYNGmS4aawQButrq6WSCRSqVTwBvRt4IknngDAJTLX1dXZ2tqKRKLy8nJhhbWZsrKyKVOmAJBIJMuXL//++++nTp1KewsC6NixI+1FKLRM0+WFF8gzz5CTJ4lIRLj2CNRGa2pIt24kOJhERvJ5vt88lZWVtLrH2LFj64wT7Koffn5+AH799VfDTWGBNkoIoSWw0tPThRbSamg57v3793MjtGGfmRrNpUuX6C/xY489duTIEW783r17KpWKNkqhBAQEhIeH3717V0C1pgm1UULItGmkc2dCPyFqo4QQQVbzXP2n//73vwJM3xpOnDgBwMXFxaBV3yxwbxRse9Q0+O233wYNGpSent6vX7+zZ8/SHj4UR0dHuVx+4sSJtLQ0hULh7OyclJS0YMGCbt26hYaG0i5GAio3Tb74AjU1WLTob4M2NgIoeeKJJ/bs2WNlZbVq1arIyEgBFLQYrtC9lZWV4WaxTBs1X+tpqkCJeX0lEEJWr149ceLEe/fuhYaGJiQkeDVxVNynTx+lUpmXlxcZGSmTyWpqaqKiosaMGePr6/vJJ5/cMKkiSELj5oZly7BpExIThZYCDB8+fM2aNYSQ119/nZ5GmCAVFRU0qGDmzJmGnclwC10BUalUAF577TWhhbSao0ePAhg+fDg3sm3bNgAv0Vs4c6C8vHzq1KkARCKRQqFoVSZrbm6uUqmkG8QAJBIJzfg2xzrcfMHd1BNC1Gri709GjnxwUy8stFSSh4eHabZIoWXxRowYYeiJLNNGjx8/DmDo0KFCC2k1tCaAs7MzN3L69GkA/fv3F1BVy7l8+fKTTz4JwMHBoc2b+lz9Ie5GzNXVVaFQXL58mV+1ZoGujRJSf9ZkY2MSNlpVVUWTBmUymQkeNw0ZMgTA1q1bDT2RZdpoYWEhgI4dOwotpC30798/ODiYiwQqLS0F0K5dO9NPwjt48CBtGOnj45PBR+hNQUFBeHg4TUOkBAQEqFSqiooK/S9uLjSwUULI7NkEMAkbJYTcvHnT1dUVwAcffCCIgOrq6vPnz0dFRZWVlemOZ2RkAHB0dDRCGx7LtFFCCP17vnXrltBCeID+ml67dk1oIU2i1WqVSiVtVxscHMx7AYvExES5XE4rYOGvE6r4+Hh+ZzFNPvmE/N///W3kzh0yfjwxnRKgJ0+epHVL+W1w1CjFxcWJiYlbt25VKBQhISF+fn5ck+QGvw/vvfcegHnz5hlaErFgG6Xr+bi4OKGF8AC9TZbL5aa5A1VRQcLC1F5eE8Ri8aeffmq4sn6lpaVbt26lTasotFS7aX4s/yi++eYbes+UmJjI1zVramouXrz4yy+/rFq1aubMmYMHD6Y1pBtgZWXl7e39/PPPnz59Wve1NCTr7NmzfOlpBou1UXo2Z+5dDNVqtUKhAODk5ATA2to6ODg4MjKSS3MSnJwc0q8fAUjfvlVGq+ybnp6uUCjo3wkAGxubkJCQVhV/M0eyskinTsTXV2gdTfDGG28AeOKJJ27fvt2GlzdYZgYEBNg0FszVsWPHgICAkJCQpUuXRkZGJiYmNlqzioZh9e3bV++31SIs1kZXrVoF4P8a3A6ZFXfu3KGNeW1sbN5+++0JEyZw9y+PP/740qVLBb/NP36cdOlCANKrFzF+Sd/q6urIyMjg4GDuY3F3d1coFIJ/LAYiN5cApFs3oXU0QW1t7fDhwwEEBQU1/zWvVqtzcnJiYmLCw8PlcrlMJqPbVg/j6uoqk8nkcnl4eHhMTExOTk4LxQwbNgzAV199xcc7ezQWa6N79uwBMGHCBKGFtJHk5GQa9+Pm5nbq1Ck6mJ+fr1Qqe/bsSX/JxGJxYGCgSqUSpJe9SkWsrAhAnnuOCJtdnZeXp1QqPT09uY/lySefXLhwoSAfi+EoKSEAcXQUWkfTFBQUdOvWDcB777338KNRUVGTJ0/28fFpNBLe0dFx0KBBM2bMWLly5e7du9PS0tqczH3o0CEAIpGosLBQvzfUUizWRtPT0wH06NFDaCFtYfv27XZ2dgACAwMbbfNAj1yEykyvqiKvvUYAIhIRhYKYSAQBDZPS7Z1rLlFiLUStJgCRSIgpb10kJCTQm/FNmzY1eEipVDazzGzzhszNmzdjYmJUKtX8+fNlMhk9W6bbC/q+mRZjsTZaU1MjlUolEklVVZXQWlpBXV0d3QwFIJfLm/9CppnptE4ExQiZ6TdukKefJgCxtye7dxtunrZz48aNSZMmicViANnZ2ULL4RNbWwIQE19kb9myBYCtre2ZM2d0xzMyMiIjI1NSUtr8J1lWVpaYmLh9+/YPP/wwJCTE39/f1ta20d0AT0/PtLQ0Pt5Ni7BYGyWE9OrVC0CDTzMiIuLAgQMmGCpMCCkqKqLH0NbW1q06HEtKSnrrrbe4c8wuXVxmz1YbIhzojz+IiwsBSM+e5MIF/q/PI3R37PfffxdaCJ/QTqKmH8U3d+5culWtT8RhcXFxfHy8SqVSKBTBwcFeXl70q7EBnTp1CgwMlMvlSqVy//79WVlZxv/rtmQbnThxIoDdOksmtVrt5uYGk8yKSUlJobt7nTt3blvHGHrkIpPJRoyQ0xqU3t5k6VKSm8uPQm4zdPx4YoDuijxD8xTXr18vtBA+8fQkADH9FXZtbS1t1zFs2LCWbHHW1NTk5OTs379fqVTK5fLAwMAOHTo87JjW1tZeXl7BwcEKhUKlUsXHx5eWlhrh7TwSS7bRhQsXAlihE6ZcWVm5evVqWv6DbkKPGjUqIiKibX2+eGTHjh10M/Spp566fv26nle7fLlu8WLi5lZf0NfKikyZQg4caHsn9Orq+swZuhlqkkv5hqxevRrAggULhBbCJzS2LCVFaB0toLCwsHv37gDeeeedBg+1fJkZEBAQFhamVCojIyPT0tJM8yaSWLaN0qrXw4YNe/gh08mKoZuhtDL89OnTeTR0jYbExJCQkPolJC2QrlCQRpfg6ekkJoY0+GpPTiZJSSQvjwweXN/+lysbbPr8+uuvAMaPHy+0ED4ZNowAxFyyt5KSkuhx39y5c1esWPHqq68OHDiwqWWmn5/fCy+8sHjx4i1btpw+fdpcSutTLNlGv/vuOwA2NjZNbdAInhVz9+7dsWPHApBKpUql0kCzFBSQ8HDSt2+9mQIkIICoVEQ3MX3OHAIQufxvLxw7lowZQw4dIhIJ8fQkqakGEmgQaJlET09PoYXwybhxBCAHDwqto8V8++23YrG4QTvSBsvMxMREc+8lY8k2mpOTI5VK6c379OnTm+n2LkhWzPnz52kVTmdnZ+OchCQmErmc2NvXm6mjI5HLCU3emzOH2NoSsZicPPng+dRGCSE7d5KiIiMI5BO1Wm1tbS0Wiy0penTqVAKQyEihdbSYefPmAfDy8vrggw82bdp08uRJi2xwYMk2SghZv369g4MD9zXYp0+fL7/8sqnFpjGzYn799VcqbMCAAUbOurl3j6xfXx+0RP/bv5/MmUP69SPjx5O+fR909eFs1EyhFa9TzWsV3Sw0XPfHH4XW0TKOHTsmEolsbGyMGXskCBZuoxS62HRxceE2YprPTH84K4bH4sG0GBLdDH3llVcEXCulpxOFgvTsSe7fr7fR9HRiZUXWrKl/grnb6OTJkwHs2rVLaCG88c47BCDr1gmtowXcv3+fptutMJ1SVAbjH2GjlLq6OloMmN7pA+jWrZtCoWgqUffhrJiuXbvOnz///PnzbdZQWlr6/PPPG3oztFXQfQtqo4SQ994j7dsTuj42dxtdtGgRgGXLlgkthDcWLyYAWblSaB0tYMGCBSeh5tAAABDNSURBVAD8/f3/CZ0L/kE2ykEz02n3UG6xuXXr1qZOyYuLi1Uqlb+/P7c5QIsHt7bpcVZWVu/evQE4OTnRrm2mA2ejpaXEzY08/zwh5m+jmzdvpkt+oYXwxsqVBCCLFwut41GcOnWKNjnnsW6eKfNPtFEOGvZEAzbxV2b6uXPnmn8+F7Hh4OAQFhbWzMmVLgcOHHB0dKTfz1euXOHvTfADZ6OEkB076o+Dzd1GExIS6Hee0EJ4Y906ApCHAjFNi+rqatoV3PTbL/PFP9pGKTQz/amnntJdbDaTmV5eXr5x48ahQ4dyz+/bt294eHhxE5k9dDOUBhi//PLLpnlwrGujhBCZjPTpQ4KCzNtGS0pKALRv395i6pBu2bK9QweHmTNnCi2kOZYsWQLA19fXvMpZ6AOz0QfQnum0QDIAW1vb5sOeMjMzFQpFly5d6PMbjd4vKyubMmUKAIlEYiKboY3SwEYzM4m1NbGyMm8bJYTQf50bN24ILYQfaDXiqVOnCi2kSVJSUqysrMRi8YkTJ4TWYjyYjTaEy0ynh+kAvL29lUplU7ULa2pqdu/e/cYbbzzstpcuXfLz8wPw2GOPHTlyxPDa204DGyWELFlCALO3UZrZ3cKNF9Pn4MGDAMaNGye0kMZRq9W03tj7778vtBajwmy0SZrqmd7CBh7R0dG05FK/fv1aXrVbKNaubZjCdP8+ef55IlC3R95YsGDJgAGjf/yxLaVeTJD4+Hg0kd9sCnz66ac0c6y1p6/mDrPRR9CGnum6m6GhoaHm2A1YoyF8N/cUhv/3/8zgTKblpKSk0C9moYU0QkZGhq2trUgkspi1f8thNtpSWtgzvby8fOrUqTQDVaFQmOPhxuHDpF07MnGi0Dr44H//s4StCY7s7GyYZKEAjUYTGBgI4M033xRaiwAwG201jVaH+vPPPwkhsbGxNAHRwcFh3759QittIxcu1NcqtQCyswlA3N2F1sETt27dAtC5c2ehhTRk7dq6kSOVPXv2MpECoEZGRAgBo/WUlpbu2LHjxx9/PHv2LB2xtbWlFWr9/Pz27dtHa++bIzU1aN8eIhHu34e1tdBq9EOjgb09ampQVoa/vvjMmMrKyvbt29va2lZVVQmt5QHZ2fD3R2UlDh5Ujx/fSLs6i6eRaqmMluDo6Dhv3rwzZ87QhH2JREKLfXXt2jU2NtZ8PRSAjQ08PFBXh5wcoaXojUSCHj1ACC5fFloKH9jZ2Uml0urq6rq6OqG11EMI5s1DZSVmzsQ/00PBbFR/evfurVQqi4uLP/74423btt28ebOppttmBO0PkJUltA4+8PUFgMxMoXXwBG0HW1FRIbSQetavx9Gj6NoVa9cKLUU4mI3yg4ODw7Jly8LCwhpth2B2WJL10PdiGV8JAOimfHl5ue7g4cOHCwoKjC8mNxeLFgHAt9/isceMP7+pYAl/8wzesaTVKH0vlvGVAICWdNBdjVZXV0+ZMsXNza1Hjx5z586NiooqKyszjpi5c1FejtBQvPCCcSY0UZiNMhrBkqzHkr4S0NhqtKioaPTo0fb29leuXNmwYUNoaGiXLl1kMtnq1auTk5O1Wq2BlGzZgkOH4OSEr74y0AxmAzupZzTCrVvo2hUdO6KkRGgpelNWBkdHtGuHigpYwI5LUFDQsWPHjh49GhQUpDuu0WhSUlJiY2NjY2Pj4uLUajUdd3Z2HjVqlEwmGz9+/OOPP86XjMJC9OmD4mJERODVV/m6qrnCbJTROI89hpIS3LqFv0qvmDFubigowLVr+Cuz14yZNGnSgQMH9u3bR+t/N0pxcfHRo0djYmKOHDly/fp1btzPz2/y5NOBgfYjR6J9e71kTJ2KPXvw3HOIjtbrOpYBs1FG4wwdilOnEBeHESOElqI3o0bh+HEcPoyxY4WWoh8ajSYgICA1NfWpp56aPXv2mDFjvL29m3/JlStX6BI1JibG2vrxO3dSCYFUCn9/yGSQyfDMM/irHURL2bULL78MBwekpcHdve1vx3IQMvafYcLQ7mkqldA6+GDePLNpYdQMxcXF48aNA/CYzqF4165dQ0JCtm7dWvSo3q1qtTohIeejj8iQIUQiedDQ0NmZvPQS2biRXL/eIhlFRcTFhQDkhx94eFOWAbNRRuN89hkBiGUUPPvySwKQt94SWoceZGZm0jxjZ2fn3bt3R0ZGyuXybt26cX4qFosDAgIUCkVMTMwj276Xl5OYGKJQED+/B34KEC8vIpeTyMjmCtNMm0YAMmoUMcNyEYaC2SijcfbsIQCZMEFoHXxw8CABSFCQ0Drayv79+2kHmv79+1+9elX3oZycnPDwcJlMZmtry1mqnZ2dTCZTKpWJiYmPLI5z+TL57jsyZQpxdHzgp9bWpKSkkSdHRxOAtG9PTK8PjpCwvVFG42RkwM8PPXogO1toKXqTn49lyzBoEF5/XWgprYQQsmbNmiVLlmi12mnTpm3cuJFrHdaAysrK+Pj4I0eOxMTEXLhwgRvv3r379Onr/f2DR49G587NzaXRICUFsbGIjUVZGU6fbuQ55eVQKNC7N/79b73el4XBbJTROLW16NEjr2vXrPj4f9nammuFkooKZGXBxQXduz8YLC7G1avo29fUC6+Ul5fPmDFj3759Eolk5cqVCoWihS+8fft2XFxcbGzsb7/9lpeXN2zY1YQEDwBeXggOxsSJGD4cOovXRsjPR3o6vLzg5fVg8Pp1XL6M0aPxV18Ixl8IvBpmmDD0FPjChQtCC2k7J07UF8rTLce+fTsBiIn3Z9Jtx93mQsharTY1NXXdusqxY0m7dg/u2e3syLPPkrVrSVP/tlFRBCA9exLdrnRffEEAotG0TYslY/7hyAyD4ePjAyDL/BOACgvxySdCi2gN0dHRgwcPzsjI6Nev39mzZ2UyWduuIxKJ+vXrN39+u8OHUVaG+HgoFAgIQHU1Dh7E+++jb1+4uCA0FBs24ObNhi+/eROrVun7Xv4JMBtlNAk9Gs40/5xQuRzr1iE1VWgdLYAQsnr16kmTJt27d++ll15KSEjw9PTk5cpSKYYPh1KJxETk5yMiAjNnwtUVt28jKgpz58LdHQMG4IMPkJRU/5J338WaNZaTR2s4mI0ymsRiVqMzZ2LAAMjlMFh+OT9UVFS8+OKLixYtEolESqVyx44d7fVMNmoCFxe8+iq2bEF+PnJyoFIhJAQdOiAlBZ9/jmPH6p/25pvo1Qtz54IdoDQPs1FGk1jMalQsxtq1OHsWP/wgtJSmyc7OHjJkyJ49exwcHPbu3atQKERGOcrx8oJcjshI3L6No0ehUGDixPqHpFKsXYu4OEREGEGIGcNslNEk1EazsrKI+a9Ghg/HzJlYvBi3bwstpTEOHTo0aNCgixcv+vj4nD59eiLnZEbExgZBQVAq62tiUcaMQUgI/vMfSyhSYziYjTKaxMnJycnJqaysrLCwUGgtPPDFFxCL8eGHD0YyMhqPjjQmdDM0ODi4pKRk4sSJZ86cod9epsO6daipwaefCq3DhGE2ymgOi7mvB+DkhOXLsWnTg7OmVaswZAh698bq1bhzRwBJFRUVoaGhixYt0mq1CoVi3759Dg4OAuhoFldXfPQRvv0Wly4JLcVUYTbKaA56ymSONtroadLcuQgIwNdf1/+vhwe6dEFmJhYtgrs7XnoJR44Y7xgqJydn2LBhu3fv7tChw549e5RKpcl2oHn3Xfj6YvNmoXWYKib6z8YwEcz0sP7OHchk2LWr4bhYjPXrUVtb/7/LlyM/HzExCAmBRoPISIwbh8cfx6JFuHrVsArj4uKGDh164cIFb2/v06dPT5482bDz6YdUiu++g8l0IzU5mI0ymoM7ZRJaSCs4cwYDBuDYMSxbBjs7BARANw09IACLFiEgoD4TVCKBTIbISFy/DqUSPXrg5k2sXo2ePTFmDLZtgyEawm/YsEEmk925c2fChAlnzpyh2UomhYsLZDLY2DwYGT4cS5ZAJmOZoI0hcBYVw7ShBurh4SG0kJYSEVGf9Th8OCkoaPXLNRoSH0/kcmJnV5832bEjkcvJuXP8yKuqqpoxYwYAkUikUCg0LLPSImA2ymgOtVptbW0tFovv378vtJZHoFYThaLe++RyUlOj19Xu3iXr1hF//wd56MOGaTdt2lZWVtbma+bm5g4cOBCAvb39L7/8opc+hinBbJTxCOh9fWpqqtBCmuPOHRIURABiY8NzVfa0NKJQECcnMnDgbQC2trYhISExMTGPrOPZgLi4uC5dugDo2bNnWloanxIZQsNslPEIJkyYAOCTTz4RWkiTnDtHPDwIQNzcyJ9/GmSK+/fJrl2nRowYwWUW9e7d+4svvrh161ZLXq5SqaysrAA8++yzxcXFBpHIEA5mo4xHEBwcTI3D29s7MjKytrZWaEV/4+ef6/cxhw0j+fkGn+7SpUtLly7lOhVLJBKZTBYZGalWqxt9fnV19axZs9hmqGXDbJTxCMrKynx00gNdXFwWLlyYmZkptC5SV8fnZmgrp66LiYkJCQmha0wAbm5uCoUiOztb92l5eXmDBg2im6FRUVHG08cwLsxGGS0iOztbpVL5+/tzfhoQEKBSqcp16yEbkaIiIpMRgEilRKkURAIhhBQUFCiVyp49e9LPRCwWBwYGqlSq+/fvf/PNN87OzgB69Ohx/vx5wSQyDA+zUUbrSExMlMvlHTp0oMbh4OAQFhbW5vLsbSM1VePpSQDStSs5ccKYMzeOVqs9duxYWFhYu3bt6MfC/RAUFHT37l2hBTIMC+vFxGgL1dXVBw4c2LBhw9GjR+mvUO/evWfOnDl79uzOzTdO05tdu3Z9+OEPJSVHnnhCvHcv/tqlNAnKysp27ty5bdu2hIQEQoi/v//p06dtdKPYGZYIs1GGXmRlZW3evHnz5s23b98GYG1t/fzzz4eFhT333HMSiYTfuTQazZIlSz7//HNCyMKF25YvDzNZgzpy5Iirq2vfvn2FFsIwBsxGGTyg0WiOHTu2YcOGvXv31tXVAejWrdv06dPnzp3LVw+MsrKy6dOnHzhwQCqVrlixouVtMhkMQ8NslMEn+fn5P/300w8//JCTkwNALBYHBQWFhYWFhIRw24VtICsra/LkyZmZmc7Ozrt27QoKCuJPMoOhL8xGGfyj1WoTEhJ++umniIiIyspKAJ06dQoJCXnrrbd0z/pbyIEDB6ZPn15WVta/f/+9e/d6eHjwr5jB0ANmowwDcu/evcjIyO+///7cuXN0JCAgQC6XT5s2jTvrbwZCyJo1a5YsWaLVaqdNm7Zx40Y73WJNDIZpwGyUYQySkpK2bdsWERFRXFwMwNbWduLEiXK5fPTo0U01bisvL58xY8a+ffskEsnKlSvZZijDZGE2yjAeD4dJ+fj4zJo1a9asWbRsB8elS5cmT56ckZHh5OS0c+dOmUwmkGQG49EwG2UIwOXLl7dv37558+bc3FwAEolk1KhRcrl8ypQpUqk0Ojr61VdfLS0t9ff337t3L19n/QyGgWA2yhAMtVr922+/bdy48eDBgxqNBoCVlZWrq+uNGzcIIS+//PKmTZvYZijD9GE2yhCegoKCbdu2rVu3rqCgAIBIJPrss88++OCDprZNGQyTgtkow1TQarVfffXV+fPnR4wY8dprrwkth8FoKcxGGQwGQy9YZ1AGg8HQC2ajDAaDoRfMRhkMBkMvmI0yGAyGXjAbZTAYDL1gNspgMBh6wWyUwWAw9ILZKIPBYOjF/wc5HGaMHRX65gAAAdN6VFh0cmRraXRQS0wgcmRraXQgMjAyNC4wOS4xAAB4nHu/b+09BiAQAGImBgiQAGJpIG5gZGNIANKMzOwOGkCamZkNQrNAxJmY2BkUQHwYFyEMVY4mDtfukAGWZ0RiQGQEwQYyYirAcAEWI3AZys3AyMDIlMDEDGQzsLAysLIxsLEzsHMwcHAysHMpcHFnMHHzJPDwZjDx8jHw8ifwC2QwMQlmMAkKJQgJZzAJiySIiGYwiYoliIlnMIkzJnCyMAhwJYgLJTixAM1nZQQqFGdjZWPn4GRh4+bh5RfgYhMWERUTFxLXYgR6hgEWrFfaDti/2dpmD+KETjNwaKzLsQOx/V1MHQ6aLtoPYqc+vmLv68B7AMRe2ddu58R7FSyurypoO73Cah+IPbFs3X6hiTvAep/yKh1Q9xUBq1maIHwg8RMzWK98c/p+3jVqYPbh3uoDjelie0HsJtnWA5+5doLdUHB34wGvun9g9n636weKjSFuC2dmOnhq2WkwW+7D+QOftO6B7TUwWHxA408l2K6PnzscLk68AmZX2C52eLn+PFgNy9pLDme6TcDsW5xPHBzz5oDNUfl+1GEvp6QDiD2jfZpDbQwXmH1Iq9WhvsgZrEYMAGdzeABl6urhAAACVnpUWHRNT0wgcmRraXQgMjAyNC4wOS4xAAB4nH1VW6obMQz9zyq8gTGSLMn2501yKaXcBNq0eyj0s/unRx6S8QXTmVh47GM9j5xTiuf79dvvv+n1yPV0Son+8+u9p1+FiE4fKSbp/P7l6y1dHm/n58rl/vP2+JFEk1ScwfsZ+/a4fzxXOF0SZyNlNkzIRONQpvEcJyXdkuRqnatjWwp14wWuQJ/kVkS6po2zeBW3BVCHYffCndImWaiS0QJosAyDrtUpNLqr+spFh0asFtPiaaPMTZhWPlYAoai49F6wX5y0yQLYYBquWRHqLVRa7YVWyB4qJWOzVo0ZObv3BZIpoJQ7adEBhXVZ5pKjOFvJsGkoyhbBIZ2rJLEEVDNbbyZIrKohYytkVGizbNakMZC9NRRrhYwSbZ6r1NI8yKENaldIS5c/aWuZxdrgEXhCSx6x70q1N6QoYtJSuizDr7unoLwXGTmrDqauoFEpzVJ7rW0nCei0TFRUyjKIIabDPJmXVfgShfJsDTyhwacKsq7Cl6hTRfAARu5FhNdAGSq5ucJj0KWz8NJ2FElzJXgnALJz60uNOoDY1xrhwkV1XwDfb9dPfb/fBOf77XrcBPHK0fC4PlI5+pox9OhexrCjRxnDj05kjHr0G2O0o6sYox+twzHmBol95qkNNATLxHYNwWVitYZgndjLQ9jEUh0rPpExPsG0iXMasbaJWRqC+8QgDSEzUzSE8MQIDSEylV5DSJlqrCFEp2LquLVfC5FX5OMVggyH65QcqfuZw7+o9FzX+H7+O2B++gcuuTJBQIIwXQAAAS96VFh0U01JTEVTIHJka2l0IDIwMjQuMDkuMQAAeJwlUDmSwzAM+8qWyYzC4U1xXLrPJ9zu5AV5/IJeN7JACARwfuR6nG89z/fjsuvC5fd5XfY8T31+Lr3mU/n5PoSCXWQJcajbOpQqWvZiUuMOGWSbaq+XkGZprUMo0wSIknJxrAPs9MrhZLrXIGzhFmAxyVZhSGFsqd1Qt2TfCkgpTGVYUW08kBF+wgZjy5LeAJ0kegecukdi5ysoYusG0nsrvL+S4M960vj24WwSBQkIUvE/x3tzjribtcqtxN15L+xKNHL7gpmqCckpiUgzZTf3wWBfscBJq2v/B2eQgpAUk5HnSJg5kmIj9l1EoTg8K9jKcaWqaB8U2em6sLRFdXSL8R6AJJrGHSd6ZYKCZ67n9w9ramN6pq4WpwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7ec2273a1fc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 0  # Feel free to change this to visualize different graphs.\n",
    "\n",
    "# Visualize the graphs\n",
    "display(test_data[idx].mol)\n",
    "visualize_pyg_graph(pygs[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a9d2dd1-2f90-44cc-8859-b93c41167f33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.      0.      0.      ... 0.      0.      0.12011]\n",
      " [0.      0.      0.      ... 0.      1.      0.14007]\n",
      " [0.      0.      0.      ... 0.      1.      0.12011]\n",
      " ...\n",
      " [0.      0.      0.      ... 0.      1.      0.12011]\n",
      " [0.      0.      0.      ... 0.      1.      0.12011]\n",
      " [0.      0.      0.      ... 0.      1.      0.12011]]\n",
      "[[0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Examine the features\n",
    "print(pygs[idx].x)\n",
    "print(pygs[idx].edge_attr)"
   ]
  }
 ],
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